scholarly journals Long-term trends of hail-related weather types in an ensemble of regional climate models using a Bayesian approach

2012 ◽  
Vol 117 (D15) ◽  
pp. n/a-n/a ◽  
Author(s):  
M.-L. Kapsch ◽  
M. Kunz ◽  
R. Vitolo ◽  
T. Economou
2017 ◽  
Vol 8 (3) ◽  
pp. 889-900 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).


2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Carina Furusho-Percot ◽  
Klaus Goergen ◽  
Carl Hartick ◽  
Ketan Kulkarni ◽  
Jessica Keune ◽  
...  

AbstractApplying the Terrestrial Systems Modeling Platform, TSMP, this study provides the first simulated long-term (1996–2018), high-resolution (~12.5 km) terrestrial system climatology over Europe, which comprises variables from groundwater across the land surface to the top of the atmosphere (G2A). The data set offers an unprecedented opportunity to test hypotheses related to short- and long-range feedback processes in space and time between the different interacting compartments of the terrestrial system. The physical consistency of simulated states and fluxes in the terrestrial system constitutes the uniqueness of the data set: while most regional climate models (RCMs) have a tendency to simplify the soil moisture and groundwater representation, TSMP explicitly simulates a full 3D soil- and groundwater dynamics, closing the terrestrial water cycle from G2A. As anthopogenic impacts are excluded, the dataset may serve as a near-natural reference for global change simulations including human water use and climate change. The data set is available as netCDF files for the pan-European EURO-CORDEX domain.


2021 ◽  
Vol 13 (24) ◽  
pp. 14001
Author(s):  
Charalampos Skoulikaris

Renewable energy sources, due to their direct (e.g., wind turbines) or indirect (e.g., hydropower, with precipitation being the generator of runoff) dependence on climatic variables, are foreseen to be affected by climate change. In this research, two run-of-river small hydropower plants (SHPPs) located at different water districts in Greece are being calibrated and validated, in order to be simulated in terms of future power production under climate change conditions. In doing so, future river discharges derived by the forcing of a hydrology model, by three Regional Climate Models under two Representative Concentration Pathways, are used as inputs for the simulation of the SHPPs. The research concludes, by comparing the outputs of short-term (2031–2060) and long-term (2071–2100) future periods to a reference period (1971–2000), that in the case of a significant projected decrease in river discharges (~25–30%), a relevant important decrease in the simulated future power generation is foreseen (~20–25%). On the other hand, in the decline projections of smaller discharges (up to ~15%) the generated energy depends on the intermonthly variations of the river runoff, establishing that runoff decreases in the wet months of the year have much lower impact on the produced energy than those occurring in the dry months. The latter is attributed to the non-existence of reservoirs that control the operation of run-of-river SHPPs; nevertheless, these types of hydropower plants can partially remediate the energy losses, since they are taking advantage of low flows for hydropower production. Hence, run-of-river SHPPs are designated as important hydro-resilience assets against the projected surface water availability decrease due to climate change.


2019 ◽  
Vol 13 (3) ◽  
pp. 845-859 ◽  
Author(s):  
Baptiste Vandecrux ◽  
Michael MacFerrin ◽  
Horst Machguth ◽  
William T. Colgan ◽  
Dirk van As ◽  
...  

Abstract. A porous layer of multi-year snow known as firn covers the Greenland-ice-sheet interior. The firn layer buffers the ice-sheet contribution to sea-level rise by retaining a fraction of summer melt as liquid water and refrozen ice. In this study we quantify the Greenland ice-sheet firn air content (FAC), an indicator of meltwater retention capacity, based on 360 point observations. We quantify FAC in both the uppermost 10 m and the entire firn column before interpolating FAC over the entire ice-sheet firn area as an empirical function of long-term mean air temperature (Ta‾) and net snow accumulation (c˙‾). We estimate a total ice-sheet-wide FAC of 26 800±1840 km3, of which 6500±450 km3 resides within the uppermost 10 m of firn, for the 2010–2017 period. In the dry snow area (Ta‾≤-19 ∘C), FAC has not changed significantly since 1953. In the low-accumulation percolation area (Ta‾>-19 ∘C and c˙‾≤600 mm w.e. yr−1), FAC has decreased by 23±16 % between 1998–2008 and 2010–2017. This reflects a loss of firn retention capacity of between 150±100 Gt and 540±440 Gt, respectively, from the top 10 m and entire firn column. The top 10 m FACs simulated by three regional climate models (HIRHAM5, RACMO2.3p2, and MARv3.9) agree within 12 % with observations. However, model biases in the total FAC and marked regional differences highlight the need for caution when using models to quantify the current and future FAC and firn retention capacity.


Author(s):  
Andreas F. Prein ◽  
Melissa S. Bukovsky ◽  
Linda O. Mearns ◽  
Cindy L. Bruyère ◽  
James M. Done

2003 ◽  
Vol 34 (5) ◽  
pp. 399-412 ◽  
Author(s):  
M. Rummukainen ◽  
J. Räisänen ◽  
D. Bjørge ◽  
J.H. Christensen ◽  
O.B. Christensen ◽  
...  

According to global climate projections, a substantial global climate change will occur during the next decades, under the assumption of continuous anthropogenic climate forcing. Global models, although fundamental in simulating the response of the climate system to anthropogenic forcing are typically geographically too coarse to well represent many regional or local features. In the Nordic region, climate studies are conducted in each of the Nordic countries to prepare regional climate projections with more detail than in global ones. Results so far indicate larger temperature changes in the Nordic region than in the global mean, regional increases and decreases in net precipitation, longer growing season, shorter snow season etc. These in turn affect runoff, snowpack, groundwater, soil frost and moisture, and thus hydropower production potential, flooding risks etc. Regional climate models do not yet fully incorporate hydrology. Water resources studies are carried out off-line using hydrological models. This requires archived meteorological output from climate models. This paper discusses Nordic regional climate scenarios for use in regional water resources studies. Potential end-users of water resources scenarios are the hydropower industry, dam safety instances and planners of other lasting infrastructure exposed to precipitation, river flows and flooding.


2021 ◽  
Author(s):  
Kelly Mahoney ◽  
James D. Scott ◽  
Michael Alexander ◽  
Rachel McCrary ◽  
Mimi Hughes ◽  
...  

AbstractUnderstanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.


Author(s):  
Weijia Qian ◽  
Howard H. Chang

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.


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